为了好玩，只需要一行语句。

(lambda iterable: reduce(lambda (uniq, dup), item: (uniq, dup | {item}) if item in uniq else (uniq | {item}, dup), iterable, (set(), set())))(some_iterable)

集合。Counter是python 2.7中的新功能:

Python 2.5.4 (r254:67916, May 31 2010, 15:03:39) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-46)] on linux2
a = [1,2,3,2,1,5,6,5,5,5]
import collections
print [x for x, y in collections.Counter(a).items() if y > 1]
Type "help", "copyright", "credits" or "license" for more information.
  File "", line 1, in 
AttributeError: 'module' object has no attribute 'Counter'
>>> 

在早期版本中，你可以使用传统的字典:

a = [1,2,3,2,1,5,6,5,5,5]
d = {}
for elem in a:
    if elem in d:
        d[elem] += 1
    else:
        d[elem] = 1

print [x for x, y in d.items() if y > 1]

要删除重复项，请使用集合(a)。要打印副本，可以这样做:

a = [1,2,3,2,1,5,6,5,5,5]

import collections
print([item for item, count in collections.Counter(a).items() if count > 1])

## [1, 2, 5]

请注意Counter并不是特别有效(计时)，可能会在这里过度使用。Set会表现得更好。这段代码以源顺序计算一个唯一元素的列表:

seen = set()
uniq = []
for x in a:
    if x not in seen:
        uniq.append(x)
        seen.add(x)

或者，更简洁地说:

seen = set()
uniq = [x for x in a if x not in seen and not seen.add(x)]    

我不推荐后一种风格，因为它不清楚not seen.add(x)在做什么(set add()方法总是返回None，因此需要not)。

计算没有库的重复元素列表:

seen = set()
dupes = []

for x in a:
    if x in seen:
        dupes.append(x)
    else:
        seen.add(x)

或者，更简洁地说:

seen = set()
dupes = [x for x in a if x in seen or seen.add(x)]    

如果列表元素不可哈希，则不能使用set /dicts，必须使用二次时间解决方案(逐个比较)。例如:

a = [[1], [2], [3], [1], [5], [3]]

no_dupes = [x for n, x in enumerate(a) if x not in a[:n]]
print no_dupes # [[1], [2], [3], [5]]

dupes = [x for n, x in enumerate(a) if x in a[:n]]
print dupes # [[1], [3]]

我注意到大多数解决方案的复杂度为O(n * n)，对于大型列表来说非常缓慢。所以我想分享一下我写的函数，它支持整数或字符串，在最好的情况下是O(n)。对于一个包含10万个元素的列表，最上面的解决方案需要超过30秒，而我的解决方案只需0.12秒

def get_duplicates(list1):
    '''Return all duplicates given a list. O(n) complexity for best case scenario.
    input: [1, 1, 1, 2, 3, 4, 4]
    output: [1, 1, 4]
    '''
    dic = {}
    for el in list1:
        try:
            dic[el] += 1
        except:
            dic[el] = 1
    dupes = []
    for key in dic.keys():
        for i in range(dic[key] - 1):
            dupes.append(key)
    return dupes


list1 = [1, 1, 1, 2, 3, 4, 4]
> print(get_duplicates(list1))
[1, 1, 4]

或者获得唯一的副本:

> print(list(set(get_duplicates(list1))))
[1, 4]

简单地检查，对于所有列表项，如果一个项的第一个索引等于该项的最后一个索引:

>>> lastindex = lambda arr, el: len(arr) - arr[::-1].index(el) -1
>>> is_duplicate  = lambda arr, el: arr.index(el) != lastindex(arr, el)
>>> duplicates = lambda arr: [*set(x for x in arr if is_duplicate(arr, x))]
>>> 
>>> a=[2,3,5,7,11,13, 2,17,7,7,17,18,3,19,5,2,7,48,48,2,19]
>>> duplicates(a)
[2, 3, 5, 7, 48, 17, 19]
>>> 

你不需要计数，只需要该物品之前是否被看到过。把这个答案用在这个问题上:

def list_duplicates(seq):
  seen = set()
  seen_add = seen.add
  # adds all elements it doesn't know yet to seen and all other to seen_twice
  seen_twice = set( x for x in seq if x in seen or seen_add(x) )
  # turn the set into a list (as requested)
  return list( seen_twice )

a = [1,2,3,2,1,5,6,5,5,5]
list_duplicates(a) # yields [1, 2, 5]

以防速度很重要，这里有一些时间安排:

# file: test.py
import collections

def thg435(l):
    return [x for x, y in collections.Counter(l).items() if y > 1]

def moooeeeep(l):
    seen = set()
    seen_add = seen.add
    # adds all elements it doesn't know yet to seen and all other to seen_twice
    seen_twice = set( x for x in l if x in seen or seen_add(x) )
    # turn the set into a list (as requested)
    return list( seen_twice )

def RiteshKumar(l):
    return list(set([x for x in l if l.count(x) > 1]))

def JohnLaRooy(L):
    seen = set()
    seen2 = set()
    seen_add = seen.add
    seen2_add = seen2.add
    for item in L:
        if item in seen:
            seen2_add(item)
        else:
            seen_add(item)
    return list(seen2)

l = [1,2,3,2,1,5,6,5,5,5]*100

以下是结果:(做得好@JohnLaRooy!)

$ python -mtimeit -s 'import test' 'test.JohnLaRooy(test.l)'
10000 loops, best of 3: 74.6 usec per loop
$ python -mtimeit -s 'import test' 'test.moooeeeep(test.l)'
10000 loops, best of 3: 91.3 usec per loop
$ python -mtimeit -s 'import test' 'test.thg435(test.l)'
1000 loops, best of 3: 266 usec per loop
$ python -mtimeit -s 'import test' 'test.RiteshKumar(test.l)'
100 loops, best of 3: 8.35 msec per loop

有趣的是，除了计时本身，当使用pypy时，排名也略有变化。最有趣的是，基于counter的方法极大地受益于pypy的优化，而我建议的方法缓存方法似乎几乎没有任何效果。

$ pypy -mtimeit -s 'import test' 'test.JohnLaRooy(test.l)'
100000 loops, best of 3: 17.8 usec per loop
$ pypy -mtimeit -s 'import test' 'test.thg435(test.l)'
10000 loops, best of 3: 23 usec per loop
$ pypy -mtimeit -s 'import test' 'test.moooeeeep(test.l)'
10000 loops, best of 3: 39.3 usec per loop

显然，这种效应与输入数据的“重复性”有关。我设置了l = [random.randrange(1000000) for I in xrange(10000)]，得到了这些结果:

$ pypy -mtimeit -s 'import test' 'test.moooeeeep(test.l)'
1000 loops, best of 3: 495 usec per loop
$ pypy -mtimeit -s 'import test' 'test.JohnLaRooy(test.l)'
1000 loops, best of 3: 499 usec per loop
$ pypy -mtimeit -s 'import test' 'test.thg435(test.l)'
1000 loops, best of 3: 1.68 msec per loop